Overview

Dataset statistics

Number of variables34
Number of observations68724
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.1 MiB
Average record size in memory276.0 B

Variable types

Numeric15
Categorical19

Alerts

antiguidade is highly overall correlated with distancia_puerta_solHigh correlation
area_construida is highly overall correlated with n_banos and 2 other fieldsHigh correlation
ascensor is highly overall correlated with precioHigh correlation
cat_calidad is highly overall correlated with precioHigh correlation
cat_n_max_pisos is highly overall correlated with cat_n_vecinosHigh correlation
cat_n_vecinos is highly overall correlated with cat_n_max_pisosHigh correlation
distancia_castellana is highly overall correlated with distancia_puerta_solHigh correlation
distancia_puerta_sol is highly overall correlated with antiguidade and 1 other fieldsHigh correlation
jardin is highly overall correlated with piscinaHigh correlation
n_banos is highly overall correlated with area_construida and 2 other fieldsHigh correlation
n_habitaciones is highly overall correlated with area_construida and 1 other fieldsHigh correlation
parking is highly overall correlated with piscinaHigh correlation
piscina is highly overall correlated with jardin and 1 other fieldsHigh correlation
precio is highly overall correlated with area_construida and 3 other fieldsHigh correlation
amueblado is highly imbalanced (79.4%)Imbalance
orientacion_n is highly imbalanced (50.1%)Imbalance
duplex is highly imbalanced (82.5%)Imbalance
estudio is highly imbalanced (81.6%)Imbalance
arico is highly imbalanced (84.4%)Imbalance
precio_parking is highly skewed (γ1 = 54.93360033)Skewed
distancia_puerta_sol is highly skewed (γ1 = 34.8675567)Skewed
distancia_metro is highly skewed (γ1 = 212.939587)Skewed
distancia_castellana is highly skewed (γ1 = 53.16800217)Skewed
n_habitaciones has 1985 (2.9%) zerosZeros
n_piso has 7619 (11.1%) zerosZeros

Reproduction

Analysis started2024-02-11 21:11:54.585007
Analysis finished2024-02-11 21:13:56.647190
Duration2 minutes and 2.06 seconds
Software versionydata-profiling vv4.6.4
Download configurationconfig.json

Variables

precio
Real number (ℝ)

HIGH CORRELATION 

Distinct2545
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396182.98
Minimum24000
Maximum8133000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:13:56.954843image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum24000
5-th percentile98000
Q1160000
median264000
Q3464000
95-th percentile1145000
Maximum8133000
Range8109000
Interquartile range (IQR)304000

Descriptive statistics

Standard deviation416577.62
Coefficient of variation (CV)1.0514778
Kurtosis28.768612
Mean396182.98
Median Absolute Deviation (MAD)124000
Skewness4.0246971
Sum2.7227279 × 1010
Variance1.7353692 × 1011
MonotonicityNot monotonic
2024-02-11T21:13:57.403538image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137000 333
 
0.5%
127000 319
 
0.5%
128000 305
 
0.4%
138000 296
 
0.4%
130000 287
 
0.4%
158000 281
 
0.4%
157000 279
 
0.4%
162000 279
 
0.4%
133000 276
 
0.4%
142000 274
 
0.4%
Other values (2535) 65795
95.7%
ValueCountFrequency (%)
24000 2
 
< 0.1%
25000 1
 
< 0.1%
29000 3
< 0.1%
33000 3
< 0.1%
34000 1
 
< 0.1%
35000 1
 
< 0.1%
36000 2
 
< 0.1%
38000 1
 
< 0.1%
39000 5
< 0.1%
40000 4
< 0.1%
ValueCountFrequency (%)
8133000 1
< 0.1%
7124000 1
< 0.1%
7044000 1
< 0.1%
7018000 1
< 0.1%
6996000 1
< 0.1%
6970000 1
< 0.1%
6848000 1
< 0.1%
6829000 1
< 0.1%
6729000 1
< 0.1%
6702000 1
< 0.1%

area_construida
Real number (ℝ)

HIGH CORRELATION 

Distinct537
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.98017
Minimum21
Maximum951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:13:57.966781image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum21
5-th percentile40
Q162
median83
Q3117
95-th percentile225
Maximum951
Range930
Interquartile range (IQR)55

Descriptive statistics

Standard deviation66.157082
Coefficient of variation (CV)0.65514927
Kurtosis15.600991
Mean100.98017
Median Absolute Deviation (MAD)25
Skewness3.0374757
Sum6939761
Variance4376.7595
MonotonicityNot monotonic
2024-02-11T21:13:58.377303image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60 2351
 
3.4%
70 2211
 
3.2%
80 1997
 
2.9%
65 1791
 
2.6%
75 1774
 
2.6%
90 1626
 
2.4%
50 1452
 
2.1%
100 1437
 
2.1%
55 1281
 
1.9%
110 1169
 
1.7%
Other values (527) 51635
75.1%
ValueCountFrequency (%)
21 46
 
0.1%
22 44
 
0.1%
23 42
 
0.1%
24 54
 
0.1%
25 174
 
0.3%
26 51
 
0.1%
27 101
 
0.1%
28 114
 
0.2%
29 56
 
0.1%
30 492
0.7%
ValueCountFrequency (%)
951 1
 
< 0.1%
941 1
 
< 0.1%
934 1
 
< 0.1%
894 1
 
< 0.1%
885 1
 
< 0.1%
850 1
 
< 0.1%
847 1
 
< 0.1%
806 1
 
< 0.1%
805 3
< 0.1%
801 1
 
< 0.1%

n_habitaciones
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.580234
Minimum0
Maximum93
Zeros1985
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:13:58.744665image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q12
median3
Q33
95-th percentile4
Maximum93
Range93
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.2625033
Coefficient of variation (CV)0.48929799
Kurtosis392.03907
Mean2.580234
Median Absolute Deviation (MAD)1
Skewness6.3408376
Sum177324
Variance1.5939146
MonotonicityNot monotonic
2024-02-11T21:13:59.125520image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3 24488
35.6%
2 20790
30.3%
1 9640
 
14.0%
4 8422
 
12.3%
5 2427
 
3.5%
0 1985
 
2.9%
6 566
 
0.8%
7 202
 
0.3%
8 111
 
0.2%
9 30
 
< 0.1%
Other values (11) 63
 
0.1%
ValueCountFrequency (%)
0 1985
 
2.9%
1 9640
 
14.0%
2 20790
30.3%
3 24488
35.6%
4 8422
 
12.3%
5 2427
 
3.5%
6 566
 
0.8%
7 202
 
0.3%
8 111
 
0.2%
9 30
 
< 0.1%
ValueCountFrequency (%)
93 1
 
< 0.1%
33 1
 
< 0.1%
20 2
 
< 0.1%
18 2
 
< 0.1%
16 1
 
< 0.1%
15 1
 
< 0.1%
14 4
 
< 0.1%
13 4
 
< 0.1%
12 12
< 0.1%
11 14
< 0.1%

n_banos
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.5828677
Minimum0
Maximum20
Zeros45
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:13:59.484871image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q11
median1
Q32
95-th percentile3
Maximum20
Range20
Interquartile range (IQR)1

Descriptive statistics

Standard deviation0.845595
Coefficient of variation (CV)0.5342171
Kurtosis17.508376
Mean1.5828677
Median Absolute Deviation (MAD)0
Skewness2.5734326
Sum108781
Variance0.7150309
MonotonicityNot monotonic
2024-02-11T21:13:59.870958image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 39024
56.8%
2 22629
32.9%
3 4737
 
6.9%
4 1569
 
2.3%
5 540
 
0.8%
6 112
 
0.2%
0 45
 
0.1%
7 20
 
< 0.1%
8 15
 
< 0.1%
11 13
 
< 0.1%
Other values (8) 20
 
< 0.1%
ValueCountFrequency (%)
0 45
 
0.1%
1 39024
56.8%
2 22629
32.9%
3 4737
 
6.9%
4 1569
 
2.3%
5 540
 
0.8%
6 112
 
0.2%
7 20
 
< 0.1%
8 15
 
< 0.1%
9 5
 
< 0.1%
ValueCountFrequency (%)
20 1
 
< 0.1%
18 1
 
< 0.1%
16 1
 
< 0.1%
14 2
 
< 0.1%
13 2
 
< 0.1%
12 2
 
< 0.1%
11 13
< 0.1%
10 6
 
< 0.1%
9 5
 
< 0.1%
8 15
< 0.1%

terraza
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
43502 
1
25222 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 43502
63.3%
1 25222
36.7%

Length

2024-02-11T21:14:00.263949image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:00.614255image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 43502
63.3%
1 25222
36.7%

Most occurring characters

ValueCountFrequency (%)
0 43502
63.3%
1 25222
36.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 43502
63.3%
1 25222
36.7%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 43502
63.3%
1 25222
36.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 43502
63.3%
1 25222
36.7%

ascensor
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
48590 
0
20134 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
1 48590
70.7%
0 20134
29.3%

Length

2024-02-11T21:14:00.965755image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:01.300515image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 48590
70.7%
0 20134
29.3%

Most occurring characters

ValueCountFrequency (%)
1 48590
70.7%
0 20134
29.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48590
70.7%
0 20134
29.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 48590
70.7%
0 20134
29.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 48590
70.7%
0 20134
29.3%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
37118 
1
31606 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 37118
54.0%
1 31606
46.0%

Length

2024-02-11T21:14:01.666507image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:01.996776image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 37118
54.0%
1 31606
46.0%

Most occurring characters

ValueCountFrequency (%)
0 37118
54.0%
1 31606
46.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 37118
54.0%
1 31606
46.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 37118
54.0%
1 31606
46.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 37118
54.0%
1 31606
46.0%

amueblado
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
3
65201 
2
 
2877
1
 
646

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 65201
94.9%
2 2877
 
4.2%
1 646
 
0.9%

Length

2024-02-11T21:14:02.352369image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:02.701116image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
3 65201
94.9%
2 2877
 
4.2%
1 646
 
0.9%

Most occurring characters

ValueCountFrequency (%)
3 65201
94.9%
2 2877
 
4.2%
1 646
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 65201
94.9%
2 2877
 
4.2%
1 646
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
3 65201
94.9%
2 2877
 
4.2%
1 646
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 65201
94.9%
2 2877
 
4.2%
1 646
 
0.9%

parking
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
53546 
1
15178 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 53546
77.9%
1 15178
 
22.1%

Length

2024-02-11T21:14:03.047150image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:03.385192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 53546
77.9%
1 15178
 
22.1%

Most occurring characters

ValueCountFrequency (%)
0 53546
77.9%
1 15178
 
22.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 53546
77.9%
1 15178
 
22.1%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 53546
77.9%
1 15178
 
22.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 53546
77.9%
1 15178
 
22.1%

precio_parking
Real number (ℝ)

SKEWED 

Distinct141
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean745.69232
Minimum1
Maximum925001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:03.765738image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q31
95-th percentile1
Maximum925001
Range925000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation8147.149
Coefficient of variation (CV)10.925617
Kurtosis5030.4191
Mean745.69232
Median Absolute Deviation (MAD)0
Skewness54.9336
Sum51246959
Variance66376037
MonotonicityNot monotonic
2024-02-11T21:14:04.198684image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 67072
97.6%
20001 172
 
0.3%
30001 159
 
0.2%
25001 142
 
0.2%
15001 139
 
0.2%
40001 102
 
0.1%
50001 90
 
0.1%
45001 66
 
0.1%
35001 56
 
0.1%
60001 43
 
0.1%
Other values (131) 683
 
1.0%
ValueCountFrequency (%)
1 67072
97.6%
2 6
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
11 2
 
< 0.1%
16 1
 
< 0.1%
17 1
 
< 0.1%
26 3
 
< 0.1%
41 4
 
< 0.1%
51 4
 
< 0.1%
ValueCountFrequency (%)
925001 1
< 0.1%
770001 1
< 0.1%
750001 1
< 0.1%
510001 1
< 0.1%
450001 1
< 0.1%
275001 1
< 0.1%
250001 1
< 0.1%
231001 1
< 0.1%
220001 1
< 0.1%
150001 1
< 0.1%

orientacion_n
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
61175 
1
7549 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 61175
89.0%
1 7549
 
11.0%

Length

2024-02-11T21:14:04.585819image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:04.919417image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 61175
89.0%
1 7549
 
11.0%

Most occurring characters

ValueCountFrequency (%)
0 61175
89.0%
1 7549
 
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 61175
89.0%
1 7549
 
11.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 61175
89.0%
1 7549
 
11.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 61175
89.0%
1 7549
 
11.0%

orientacion_s
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
51899 
1
16825 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51899
75.5%
1 16825
 
24.5%

Length

2024-02-11T21:14:05.310688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:05.760033image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51899
75.5%
1 16825
 
24.5%

Most occurring characters

ValueCountFrequency (%)
0 51899
75.5%
1 16825
 
24.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51899
75.5%
1 16825
 
24.5%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51899
75.5%
1 16825
 
24.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51899
75.5%
1 16825
 
24.5%

orientacion_e
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
54449 
1
14275 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 54449
79.2%
1 14275
 
20.8%

Length

2024-02-11T21:14:06.121771image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:06.447128image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 54449
79.2%
1 14275
 
20.8%

Most occurring characters

ValueCountFrequency (%)
0 54449
79.2%
1 14275
 
20.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54449
79.2%
1 14275
 
20.8%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 54449
79.2%
1 14275
 
20.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 54449
79.2%
1 14275
 
20.8%

orientacion_o
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
58371 
1
10353 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 58371
84.9%
1 10353
 
15.1%

Length

2024-02-11T21:14:06.806837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:07.136430image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 58371
84.9%
1 10353
 
15.1%

Most occurring characters

ValueCountFrequency (%)
0 58371
84.9%
1 10353
 
15.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58371
84.9%
1 10353
 
15.1%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58371
84.9%
1 10353
 
15.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58371
84.9%
1 10353
 
15.1%

trastero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
51228 
1
17496 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 51228
74.5%
1 17496
 
25.5%

Length

2024-02-11T21:14:07.498390image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:07.826842image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 51228
74.5%
1 17496
 
25.5%

Most occurring characters

ValueCountFrequency (%)
0 51228
74.5%
1 17496
 
25.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 51228
74.5%
1 17496
 
25.5%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 51228
74.5%
1 17496
 
25.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 51228
74.5%
1 17496
 
25.5%

armarios
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
41397 
0
27327 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
1 41397
60.2%
0 27327
39.8%

Length

2024-02-11T21:14:08.180463image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:08.498850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 41397
60.2%
0 27327
39.8%

Most occurring characters

ValueCountFrequency (%)
1 41397
60.2%
0 27327
39.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 41397
60.2%
0 27327
39.8%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 41397
60.2%
0 27327
39.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 41397
60.2%
0 27327
39.8%

piscina
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
59083 
1
9641 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 59083
86.0%
1 9641
 
14.0%

Length

2024-02-11T21:14:08.876782image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:09.276598image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 59083
86.0%
1 9641
 
14.0%

Most occurring characters

ValueCountFrequency (%)
0 59083
86.0%
1 9641
 
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 59083
86.0%
1 9641
 
14.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 59083
86.0%
1 9641
 
14.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 59083
86.0%
1 9641
 
14.0%

portero
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
50669 
1
18055 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 50669
73.7%
1 18055
 
26.3%

Length

2024-02-11T21:14:09.645331image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:09.978422image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 50669
73.7%
1 18055
 
26.3%

Most occurring characters

ValueCountFrequency (%)
0 50669
73.7%
1 18055
 
26.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50669
73.7%
1 18055
 
26.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50669
73.7%
1 18055
 
26.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50669
73.7%
1 18055
 
26.3%

jardin
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
56265 
1
12459 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 56265
81.9%
1 12459
 
18.1%

Length

2024-02-11T21:14:10.335807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:10.667844image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 56265
81.9%
1 12459
 
18.1%

Most occurring characters

ValueCountFrequency (%)
0 56265
81.9%
1 12459
 
18.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56265
81.9%
1 12459
 
18.1%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56265
81.9%
1 12459
 
18.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56265
81.9%
1 12459
 
18.1%

duplex
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
66924 
1
 
1800

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 66924
97.4%
1 1800
 
2.6%

Length

2024-02-11T21:14:11.027113image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:11.356493image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66924
97.4%
1 1800
 
2.6%

Most occurring characters

ValueCountFrequency (%)
0 66924
97.4%
1 1800
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66924
97.4%
1 1800
 
2.6%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66924
97.4%
1 1800
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66924
97.4%
1 1800
 
2.6%

estudio
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
66797 
1
 
1927

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
0 66797
97.2%
1 1927
 
2.8%

Length

2024-02-11T21:14:11.890192image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:12.215747image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 66797
97.2%
1 1927
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 66797
97.2%
1 1927
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66797
97.2%
1 1927
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66797
97.2%
1 1927
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66797
97.2%
1 1927
 
2.8%

arico
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
0
67163 
1
 
1561

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 67163
97.7%
1 1561
 
2.3%

Length

2024-02-11T21:14:12.557183image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:12.896486image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
0 67163
97.7%
1 1561
 
2.3%

Most occurring characters

ValueCountFrequency (%)
0 67163
97.7%
1 1561
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 67163
97.7%
1 1561
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 67163
97.7%
1 1561
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 67163
97.7%
1 1561
 
2.3%

n_piso
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7551801
Minimum-1
Maximum11
Zeros7619
Zeros (%)11.1%
Negative710
Negative (%)1.0%
Memory size1.0 MiB
2024-02-11T21:14:13.207016image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-1
5-th percentile0
Q11
median2
Q34
95-th percentile7
Maximum11
Range12
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.2614943
Coefficient of variation (CV)0.82081539
Kurtosis1.5642152
Mean2.7551801
Median Absolute Deviation (MAD)1
Skewness1.1514176
Sum189347
Variance5.1143563
MonotonicityNot monotonic
2024-02-11T21:14:13.592258image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
1 15329
22.3%
2 12856
18.7%
3 11088
16.1%
4 8828
12.8%
0 7619
11.1%
5 4661
 
6.8%
6 2878
 
4.2%
7 1854
 
2.7%
8 1146
 
1.7%
11 792
 
1.2%
Other values (3) 1673
 
2.4%
ValueCountFrequency (%)
-1 710
 
1.0%
0 7619
11.1%
1 15329
22.3%
2 12856
18.7%
3 11088
16.1%
4 8828
12.8%
5 4661
 
6.8%
6 2878
 
4.2%
7 1854
 
2.7%
8 1146
 
1.7%
ValueCountFrequency (%)
11 792
 
1.2%
10 342
 
0.5%
9 621
 
0.9%
8 1146
 
1.7%
7 1854
 
2.7%
6 2878
 
4.2%
5 4661
 
6.8%
4 8828
12.8%
3 11088
16.1%
2 12856
18.7%

cat_n_max_pisos
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.393487
Minimum0
Maximum26
Zeros73
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:13.956066image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q15
median6
Q38
95-th percentile12
Maximum26
Range26
Interquartile range (IQR)3

Descriptive statistics

Standard deviation2.8491185
Coefficient of variation (CV)0.44562827
Kurtosis5.7615344
Mean6.393487
Median Absolute Deviation (MAD)1
Skewness1.7585823
Sum439386
Variance8.1174763
MonotonicityNot monotonic
2024-02-11T21:14:14.350438image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5 15386
22.4%
7 10091
14.7%
4 9991
14.5%
6 9812
14.3%
8 6938
10.1%
3 3914
 
5.7%
9 3724
 
5.4%
10 2091
 
3.0%
2 1306
 
1.9%
11 1143
 
1.7%
Other values (16) 4328
 
6.3%
ValueCountFrequency (%)
0 73
 
0.1%
1 416
 
0.6%
2 1306
 
1.9%
3 3914
 
5.7%
4 9991
14.5%
5 15386
22.4%
6 9812
14.3%
7 10091
14.7%
8 6938
10.1%
9 3724
 
5.4%
ValueCountFrequency (%)
26 32
 
< 0.1%
25 21
 
< 0.1%
23 67
 
0.1%
22 48
 
0.1%
21 125
0.2%
20 73
 
0.1%
19 13
 
< 0.1%
18 68
 
0.1%
17 222
0.3%
16 184
0.3%

cat_n_vecinos
Real number (ℝ)

HIGH CORRELATION 

Distinct328
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.755311
Minimum1
Maximum1499
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:14.747530image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5
Q112
median21
Q339
95-th percentile138
Maximum1499
Range1498
Interquartile range (IQR)27

Descriptive statistics

Standard deviation53.272563
Coefficient of variation (CV)1.3745874
Kurtosis33.288602
Mean38.755311
Median Absolute Deviation (MAD)11
Skewness4.3455507
Sum2663420
Variance2837.9659
MonotonicityNot monotonic
2024-02-11T21:14:15.193932image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11 3461
 
5.0%
21 3006
 
4.4%
9 2797
 
4.1%
13 2404
 
3.5%
17 2079
 
3.0%
16 2010
 
2.9%
15 1821
 
2.6%
7 1817
 
2.6%
14 1774
 
2.6%
10 1738
 
2.5%
Other values (318) 45817
66.7%
ValueCountFrequency (%)
1 487
 
0.7%
2 775
 
1.1%
3 802
 
1.2%
4 957
 
1.4%
5 1055
 
1.5%
6 846
 
1.2%
7 1817
2.6%
8 1115
 
1.6%
9 2797
4.1%
10 1738
2.5%
ValueCountFrequency (%)
1499 1
 
< 0.1%
724 16
< 0.1%
701 1
 
< 0.1%
638 4
 
< 0.1%
574 33
< 0.1%
518 1
 
< 0.1%
512 1
 
< 0.1%
503 8
 
< 0.1%
501 8
 
< 0.1%
478 15
< 0.1%

cat_calidad
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8419038
Minimum0
Maximum9
Zeros282
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:15.546546image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3
Q14
median5
Q36
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.4532837
Coefficient of variation (CV)0.30014717
Kurtosis-0.022485242
Mean4.8419038
Median Absolute Deviation (MAD)1
Skewness-0.055044988
Sum332755
Variance2.1120336
MonotonicityNot monotonic
2024-02-11T21:14:15.914233image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
4 17948
26.1%
5 15357
22.3%
6 14807
21.5%
3 9018
13.1%
7 7381
10.7%
2 1993
 
2.9%
8 1069
 
1.6%
1 468
 
0.7%
9 401
 
0.6%
0 282
 
0.4%
ValueCountFrequency (%)
0 282
 
0.4%
1 468
 
0.7%
2 1993
 
2.9%
3 9018
13.1%
4 17948
26.1%
5 15357
22.3%
6 14807
21.5%
7 7381
10.7%
8 1069
 
1.6%
9 401
 
0.6%
ValueCountFrequency (%)
9 401
 
0.6%
8 1069
 
1.6%
7 7381
10.7%
6 14807
21.5%
5 15357
22.3%
4 17948
26.1%
3 9018
13.1%
2 1993
 
2.9%
1 468
 
0.7%
0 282
 
0.4%

distancia_puerta_sol
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct68671
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.4239086
Minimum0.0076465716
Maximum415.75258
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:16.295712image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0076465716
5-th percentile0.757535
Q12.3599428
median4.0346406
Q36.1328881
95-th percentile9.0781218
Maximum415.75258
Range415.74494
Interquartile range (IQR)3.7729454

Descriptive statistics

Standard deviation3.0851471
Coefficient of variation (CV)0.6973804
Kurtosis4596.7434
Mean4.4239086
Median Absolute Deviation (MAD)1.8188619
Skewness34.867557
Sum304028.7
Variance9.5181329
MonotonicityNot monotonic
2024-02-11T21:14:16.716751image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.89496767 2
 
< 0.1%
1.951229271 2
 
< 0.1%
4.604657717 2
 
< 0.1%
5.128974519 2
 
< 0.1%
4.692164706 2
 
< 0.1%
8.678312755 2
 
< 0.1%
2.345176122 2
 
< 0.1%
2.557221127 2
 
< 0.1%
0.8146167651 2
 
< 0.1%
7.522194534 2
 
< 0.1%
Other values (68661) 68704
> 99.9%
ValueCountFrequency (%)
0.007646571605 1
< 0.1%
0.01703500572 1
< 0.1%
0.01994969235 1
< 0.1%
0.02546122988 1
< 0.1%
0.02558615768 1
< 0.1%
0.02854700518 1
< 0.1%
0.02911750986 1
< 0.1%
0.03200618324 1
< 0.1%
0.03215794221 1
< 0.1%
0.03256324357 1
< 0.1%
ValueCountFrequency (%)
415.7525844 1
< 0.1%
14.04881782 1
< 0.1%
13.96898197 1
< 0.1%
13.40740891 1
< 0.1%
13.36091782 1
< 0.1%
13.34529239 1
< 0.1%
13.31904647 1
< 0.1%
13.30643156 1
< 0.1%
13.27655199 1
< 0.1%
13.24294284 1
< 0.1%

distancia_metro
Real number (ℝ)

SKEWED 

Distinct68532
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.46978629
Minimum0.0014160887
Maximum399.47737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:17.103863image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014160887
5-th percentile0.094901557
Q10.21109507
median0.32726675
Q30.51365435
95-th percentile1.1661692
Maximum399.47737
Range399.47595
Interquartile range (IQR)0.30255928

Descriptive statistics

Standard deviation1.6320414
Coefficient of variation (CV)3.474008
Kurtosis51989.837
Mean0.46978629
Median Absolute Deviation (MAD)0.13738973
Skewness212.93959
Sum32285.593
Variance2.663559
MonotonicityNot monotonic
2024-02-11T21:14:17.556402image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.1351915238 3
 
< 0.1%
0.2808148905 2
 
< 0.1%
0.05243663977 2
 
< 0.1%
0.6235387197 2
 
< 0.1%
0.23724794 2
 
< 0.1%
0.2153036321 2
 
< 0.1%
0.1920359235 2
 
< 0.1%
0.1986262657 2
 
< 0.1%
0.101487036 2
 
< 0.1%
0.3389193947 2
 
< 0.1%
Other values (68522) 68703
> 99.9%
ValueCountFrequency (%)
0.001416088655 2
< 0.1%
0.002588903776 1
< 0.1%
0.004017688228 1
< 0.1%
0.004132945902 1
< 0.1%
0.004376046082 1
< 0.1%
0.004477056832 1
< 0.1%
0.004687972017 1
< 0.1%
0.004969505909 1
< 0.1%
0.004995792581 1
< 0.1%
0.005002116943 1
< 0.1%
ValueCountFrequency (%)
399.4773665 1
< 0.1%
9.42521385 1
< 0.1%
9.334654359 1
< 0.1%
9.329834263 1
< 0.1%
8.98225003 1
< 0.1%
8.969796872 1
< 0.1%
8.910706764 1
< 0.1%
8.901006008 1
< 0.1%
8.894902948 1
< 0.1%
8.889994953 1
< 0.1%

distancia_castellana
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct68667
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6269668
Minimum0.0014350974
Maximum412.80369
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:17.961356image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0.0014350974
5-th percentile0.26145458
Q11.016944
median1.9046783
Q33.7710503
95-th percentile7.018666
Maximum412.80369
Range412.80225
Interquartile range (IQR)2.7541063

Descriptive statistics

Standard deviation2.6740629
Coefficient of variation (CV)1.0179279
Kurtosis8055.0266
Mean2.6269668
Median Absolute Deviation (MAD)1.1576681
Skewness53.168002
Sum180535.67
Variance7.1506123
MonotonicityNot monotonic
2024-02-11T21:14:18.390049image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.03652147671 2
 
< 0.1%
0.03723037431 2
 
< 0.1%
1.151967651 2
 
< 0.1%
0.7313259386 2
 
< 0.1%
1.693900624 2
 
< 0.1%
9.352469146 2
 
< 0.1%
1.942870881 2
 
< 0.1%
1.577289494 2
 
< 0.1%
1.179946374 2
 
< 0.1%
1.198837622 2
 
< 0.1%
Other values (68657) 68704
> 99.9%
ValueCountFrequency (%)
0.001435097407 1
< 0.1%
0.004269475612 1
< 0.1%
0.004322044394 1
< 0.1%
0.007929002888 1
< 0.1%
0.008344202002 1
< 0.1%
0.008422856558 1
< 0.1%
0.008501845916 1
< 0.1%
0.008559026743 1
< 0.1%
0.008597461344 1
< 0.1%
0.008622640052 1
< 0.1%
ValueCountFrequency (%)
412.8036884 1
< 0.1%
12.43845321 1
< 0.1%
12.381502 1
< 0.1%
12.299799 1
< 0.1%
12.27600563 1
< 0.1%
12.25727181 1
< 0.1%
12.23682269 1
< 0.1%
12.18381017 1
< 0.1%
12.17743325 1
< 0.1%
12.01493561 1
< 0.1%

longitud
Real number (ℝ)

Distinct68671
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-3.6867962
Minimum-3.8336106
Maximum-2.7533027
Zeros0
Zeros (%)0.0%
Negative68724
Negative (%)100.0%
Memory size1.0 MiB
2024-02-11T21:14:18.804540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum-3.8336106
5-th percentile-3.7470925
Q1-3.7084081
median-3.6942903
Q3-3.6668084
95-th percentile-3.6135995
Maximum-2.7533027
Range1.0803079
Interquartile range (IQR)0.0415997

Descriptive statistics

Standard deviation0.03850884
Coefficient of variation (CV)-0.010445069
Kurtosis5.5988563
Mean-3.6867962
Median Absolute Deviation (MAD)0.020476191
Skewness0.57852802
Sum-253371.38
Variance0.0014829308
MonotonicityNot monotonic
2024-02-11T21:14:19.238212image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-3.672510993 2
 
< 0.1%
-3.690526127 2
 
< 0.1%
-3.690859736 2
 
< 0.1%
-3.676314807 2
 
< 0.1%
-3.668018161 2
 
< 0.1%
-3.779258617 2
 
< 0.1%
-3.693759497 2
 
< 0.1%
-3.678092955 2
 
< 0.1%
-3.704693733 2
 
< 0.1%
-3.696057981 2
 
< 0.1%
Other values (68661) 68704
> 99.9%
ValueCountFrequency (%)
-3.833610625 1
< 0.1%
-3.832513838 1
< 0.1%
-3.832442618 1
< 0.1%
-3.827888505 1
< 0.1%
-3.827372412 1
< 0.1%
-3.826758638 1
< 0.1%
-3.826652233 1
< 0.1%
-3.826628998 1
< 0.1%
-3.826500882 1
< 0.1%
-3.825939023 1
< 0.1%
ValueCountFrequency (%)
-2.75330272 1
< 0.1%
-3.542727613 1
< 0.1%
-3.543174003 1
< 0.1%
-3.547284336 1
< 0.1%
-3.547713871 1
< 0.1%
-3.548037968 1
< 0.1%
-3.550122513 1
< 0.1%
-3.550398228 1
< 0.1%
-3.550431233 1
< 0.1%
-3.550480353 1
< 0.1%

latitud
Real number (ℝ)

Distinct68671
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean40.4212
Minimum36.756391
Maximum40.520637
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:19.680184image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum36.756391
5-th percentile40.36764
Q140.397631
median40.423413
Q340.441565
95-th percentile40.476427
Maximum40.520637
Range3.7642454
Interquartile range (IQR)0.043933657

Descriptive statistics

Standard deviation0.036039637
Coefficient of variation (CV)0.00089160237
Kurtosis1555.0343
Mean40.4212
Median Absolute Deviation (MAD)0.022077181
Skewness-15.280213
Sum2777906.5
Variance0.0012988555
MonotonicityNot monotonic
2024-02-11T21:14:20.147135image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40.40614489 2
 
< 0.1%
40.40226391 2
 
< 0.1%
40.45676653 2
 
< 0.1%
40.45764533 2
 
< 0.1%
40.44876566 2
 
< 0.1%
40.46930339 2
 
< 0.1%
40.39695623 2
 
< 0.1%
40.40455372 2
 
< 0.1%
40.4093046 2
 
< 0.1%
40.48390637 2
 
< 0.1%
Other values (68661) 68704
> 99.9%
ValueCountFrequency (%)
36.7563914 1
< 0.1%
40.32868222 1
< 0.1%
40.32870614 1
< 0.1%
40.33165199 1
< 0.1%
40.33169949 1
< 0.1%
40.33212122 1
< 0.1%
40.33212891 1
< 0.1%
40.33214619 1
< 0.1%
40.33222099 1
< 0.1%
40.33231457 1
< 0.1%
ValueCountFrequency (%)
40.52063684 1
< 0.1%
40.52034852 1
< 0.1%
40.5202431 1
< 0.1%
40.52006389 1
< 0.1%
40.51983257 1
< 0.1%
40.51975356 1
< 0.1%
40.51959642 1
< 0.1%
40.51938411 1
< 0.1%
40.51918941 1
< 0.1%
40.51917036 1
< 0.1%

interior
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
1
59212 
0
9512 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 59212
86.2%
0 9512
 
13.8%

Length

2024-02-11T21:14:20.725236image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:21.058221image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59212
86.2%
0 9512
 
13.8%

Most occurring characters

ValueCountFrequency (%)
1 59212
86.2%
0 9512
 
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 59212
86.2%
0 9512
 
13.8%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 59212
86.2%
0 9512
 
13.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 59212
86.2%
0 9512
 
13.8%

status_inmueble
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size1.0 MiB
2
53883 
3
13140 
1
 
1701

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters68724
Distinct characters3
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row3

Common Values

ValueCountFrequency (%)
2 53883
78.4%
3 13140
 
19.1%
1 1701
 
2.5%

Length

2024-02-11T21:14:21.406918image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-11T21:14:21.767525image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
2 53883
78.4%
3 13140
 
19.1%
1 1701
 
2.5%

Most occurring characters

ValueCountFrequency (%)
2 53883
78.4%
3 13140
 
19.1%
1 1701
 
2.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68724
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 53883
78.4%
3 13140
 
19.1%
1 1701
 
2.5%

Most occurring scripts

ValueCountFrequency (%)
Common 68724
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 53883
78.4%
3 13140
 
19.1%
1 1701
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68724
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 53883
78.4%
3 13140
 
19.1%
1 1701
 
2.5%

antiguidade
Real number (ℝ)

HIGH CORRELATION 

Distinct167
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52.842704
Minimum0
Maximum391
Zeros575
Zeros (%)0.8%
Negative0
Negative (%)0.0%
Memory size1.0 MiB
2024-02-11T21:14:22.185659image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q135
median51
Q363
95-th percentile118
Maximum391
Range391
Interquartile range (IQR)28

Descriptive statistics

Standard deviation28.948525
Coefficient of variation (CV)0.54782444
Kurtosis1.5483281
Mean52.842704
Median Absolute Deviation (MAD)14
Skewness0.77869994
Sum3631562
Variance838.01708
MonotonicityNot monotonic
2024-02-11T21:14:22.676106image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
58 4388
 
6.4%
53 3535
 
5.1%
48 3432
 
5.0%
118 2533
 
3.7%
68 1594
 
2.3%
78 1524
 
2.2%
49 1445
 
2.1%
50 1400
 
2.0%
88 1378
 
2.0%
52 1303
 
1.9%
Other values (157) 46192
67.2%
ValueCountFrequency (%)
0 575
0.8%
1 286
0.4%
2 97
 
0.1%
3 123
 
0.2%
4 270
0.4%
5 126
 
0.2%
6 149
 
0.2%
7 120
 
0.2%
8 288
0.4%
9 388
0.6%
ValueCountFrequency (%)
391 1
 
< 0.1%
363 1
 
< 0.1%
326 1
 
< 0.1%
322 1
 
< 0.1%
318 1
 
< 0.1%
295 1
 
< 0.1%
288 1
 
< 0.1%
238 1
 
< 0.1%
218 6
< 0.1%
198 1
 
< 0.1%

Interactions

2024-02-11T21:13:50.010473image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:38.955718image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:44.112880image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:49.197004image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:54.196351image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:59.299581image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:04.198036image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:09.249547image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:14.335852image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:19.516664image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:24.630155image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:29.586850image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:34.884831image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:39.821519image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
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2024-02-11T21:12:43.757112image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:48.866959image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:53.860436image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:12:58.935961image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:03.868261image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:08.902690image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:13.949239image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:19.156092image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:24.264143image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:29.256955image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:34.546013image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:39.486198image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:44.396230image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-02-11T21:13:49.668161image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Correlations

2024-02-11T21:14:23.203681image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
aire_acondicionadoamuebladoantiguidadearea_construidaaricoarmariosascensorcat_calidadcat_n_max_pisoscat_n_vecinosdistancia_castellanadistancia_metrodistancia_puerta_solduplexestudiointeriorjardinlatitudlongitudn_banosn_habitacionesn_pisoorientacion_eorientacion_norientacion_oorientacion_sparkingpiscinaporteroprecioprecio_parkingstatus_inmuebleterrazatrastero
aire_acondicionado1.0000.043-0.1320.1270.0520.3340.138-0.1400.0470.069-0.0290.0220.0150.0740.0230.0560.1440.0600.0470.153-0.0370.1270.0850.0430.0770.0920.1710.1480.1650.2050.0530.2930.0440.135
amueblado0.0431.0000.030-0.0280.0010.0370.0020.007-0.001-0.011-0.028-0.024-0.0370.0000.0080.0280.024-0.000-0.023-0.023-0.011-0.0050.0120.0060.0110.0180.0340.0220.021-0.0140.0210.0480.0170.029
antiguidade-0.1320.0301.000-0.1600.0280.1670.2290.111-0.063-0.238-0.390-0.361-0.5190.1010.0720.2620.380-0.010-0.168-0.156-0.030-0.0770.0180.0140.0080.0240.4210.4380.1200.025-0.0440.1480.2150.288
area_construida0.127-0.028-0.1601.0000.0260.1510.292-0.3570.2710.146-0.0640.0530.0340.0950.0800.1700.1680.1720.0570.7760.7220.2140.0620.0480.1020.0950.2800.1790.3380.7230.0640.0760.1280.276
arico0.0520.0010.0280.0261.0000.0240.006-0.007-0.030-0.026-0.0040.002-0.0030.0760.0000.0170.0100.006-0.0070.003-0.0100.1420.0300.0260.0400.0410.0150.0080.0240.0210.0050.0240.0840.024
armarios0.3340.0370.1670.1510.0241.0000.239-0.1790.1420.140-0.0280.0270.0240.0430.0360.0610.1880.1030.0360.2190.0490.1030.1190.0820.1000.1420.1850.1420.2320.2580.0730.2680.1050.153
ascensor0.1380.0020.2290.2920.0060.2391.000-0.3860.4470.386-0.1190.001-0.0150.0150.0210.0490.2300.2150.0820.3880.1590.2260.0490.0440.0630.0410.2820.2450.3520.5270.0740.1040.0510.224
cat_calidad-0.1400.0070.111-0.357-0.007-0.179-0.3861.000-0.306-0.3550.3200.0970.2580.0450.0430.1130.214-0.215-0.009-0.395-0.089-0.1420.0360.0280.0610.0430.2880.2980.330-0.593-0.0350.0760.0850.244
cat_n_max_pisos0.047-0.001-0.0630.271-0.0300.1420.447-0.3061.0000.625-0.160-0.030-0.0460.0520.0430.1390.1860.1300.0820.2670.1720.3060.0370.0360.0530.0360.1660.1770.3240.3610.0300.0930.1270.125
cat_n_vecinos0.069-0.011-0.2380.146-0.0260.1400.386-0.3550.6251.0000.0290.0710.1270.0020.0100.0610.2740.0870.1440.1790.0620.2080.0050.0040.0000.0000.2300.3300.1050.2180.0180.0680.0130.171
distancia_castellana-0.029-0.028-0.390-0.064-0.004-0.028-0.1190.320-0.1600.0291.0000.3610.7070.0000.0000.0000.000-0.2500.166-0.1120.054-0.0410.0000.0000.0000.0000.0000.0000.000-0.438-0.0340.0000.0000.000
distancia_metro0.022-0.024-0.3610.0530.0020.0270.0010.097-0.0300.0710.3611.0000.4300.0000.0000.0000.000-0.0920.0470.0330.0660.0000.0000.0000.0000.0000.0000.0000.000-0.178-0.0120.0000.0000.000
distancia_puerta_sol0.015-0.037-0.5190.034-0.0030.024-0.0150.258-0.0460.1270.7070.4301.0000.0000.0000.0000.0000.1160.371-0.0030.1040.0210.0000.0000.0000.0000.0000.0000.000-0.317-0.0250.0000.0000.000
duplex0.0740.0000.1010.0950.0760.0430.0150.0450.0520.0020.0000.0000.0001.0000.0300.0140.0440.0370.0030.105-0.006-0.0120.0080.0040.0120.0230.0620.0530.0130.0610.0160.0520.0500.051
estudio0.0230.0080.0720.0800.0000.0360.0210.0430.0430.0100.0000.0000.0000.0301.0000.0840.0250.026-0.020-0.118-0.297-0.0820.0110.0040.0120.0000.0370.0220.016-0.0980.0130.0440.0780.049
interior0.0560.0280.2620.1700.0170.0610.0490.1130.1390.0610.0000.0000.0000.0140.0841.0000.151-0.0210.0660.1930.2630.1220.0360.0370.0410.0610.1640.1350.0330.0690.0220.0610.1900.157
jardin0.1440.0240.3800.1680.0100.1880.2300.2140.1860.2740.0000.0000.0000.0440.0250.1511.0000.1080.1340.2510.0980.0780.0550.0570.0550.0680.4800.6710.2420.1740.0330.1720.1240.355
latitud0.060-0.000-0.0100.1720.0060.1030.215-0.2150.1300.087-0.250-0.0920.1160.0370.026-0.0210.1081.0000.1730.1950.0400.0400.0000.0000.0000.0000.0000.0000.0000.4510.0300.0000.0000.000
longitud0.047-0.023-0.1680.057-0.0070.0360.082-0.0090.0820.1440.1660.0470.3710.003-0.0200.0660.1340.1731.0000.0700.0240.0390.0000.0100.0140.0080.1880.2370.1030.023-0.0050.1110.1150.142
n_banos0.153-0.023-0.1560.7760.0030.2190.388-0.3950.2670.179-0.1120.033-0.0030.105-0.1180.1930.2510.1950.0701.0000.5620.1560.0430.0450.0700.0680.2050.1110.2620.6730.0620.0420.1040.205
n_habitaciones-0.037-0.011-0.0300.722-0.0100.0490.159-0.0890.1720.0620.0540.0660.104-0.006-0.2970.2630.0980.0400.0240.5621.0000.1790.0000.0050.0110.0000.0090.0120.0060.3960.0130.0110.0030.006
n_piso0.127-0.005-0.0770.2140.1420.1030.226-0.1420.3060.208-0.0410.0000.021-0.012-0.0820.1220.0780.0400.0390.1560.1791.0000.0470.0510.0670.0580.1350.1130.2000.2350.0230.0240.1970.096
orientacion_e0.0850.0120.0180.0620.0300.1190.0490.0360.0370.0050.0000.0000.0000.0080.0110.0360.0550.0000.0000.0430.0000.0471.0000.0840.1050.1090.0350.0130.0770.0750.0320.0820.0590.046
orientacion_n0.0430.0060.0140.0480.0260.0820.0440.0280.0360.0040.0000.0000.0000.0040.0040.0370.0570.0000.0100.0450.0050.0510.0841.0000.1130.0480.0390.0330.0520.0650.0190.0580.0470.034
orientacion_o0.0770.0110.0080.1020.0400.1000.0630.0610.0530.0000.0000.0000.0000.0120.0120.0410.0550.0000.0140.0700.0110.0670.1050.1131.0000.0520.0440.0260.0820.1150.0350.0730.0510.054
orientacion_s0.0920.0180.0240.0950.0410.1420.0410.0430.0360.0000.0000.0000.0000.0230.0000.0610.0680.0000.0080.0680.0000.0580.1090.0480.0521.0000.0460.0270.0850.0940.0250.0870.0740.052
parking0.1710.0340.4210.2800.0150.1850.2820.2880.1660.2300.0000.0000.0000.0620.0370.1640.4800.0000.1880.2050.0090.1350.0350.0390.0440.0461.0000.5430.2350.289-0.0820.2040.1220.441
piscina0.1480.0220.4380.1790.0080.1420.2450.2980.1770.3300.0000.0000.0000.0530.0220.1350.6710.0000.2370.1110.0120.1130.0130.0330.0260.0270.5431.0000.2430.2220.0180.2760.0780.412
portero0.1650.0210.1200.3380.0240.2320.3520.3300.3240.1050.0000.0000.0000.0130.0160.0330.2420.0000.1030.2620.0060.2000.0770.0520.0820.0850.2350.2431.0000.4370.0640.1000.0670.179
precio0.205-0.0140.0250.7230.0210.2580.527-0.5930.3610.218-0.438-0.178-0.3170.061-0.0980.0690.1740.4510.0230.6730.3960.2350.0750.0650.1150.0940.2890.2220.4371.0000.0790.0450.0620.182
precio_parking0.0530.021-0.0440.0640.0050.0730.074-0.0350.0300.018-0.034-0.012-0.0250.0160.0130.0220.0330.030-0.0050.0620.0130.0230.0320.0190.0350.025-0.0820.0180.0640.0791.0000.0000.0000.014
status_inmueble0.2930.0480.1480.0760.0240.2680.1040.0760.0930.0680.0000.0000.0000.0520.0440.0610.1720.0000.1110.0420.0110.0240.0820.0580.0730.0870.2040.2760.1000.0450.0001.0000.0490.188
terraza0.0440.0170.2150.1280.0840.1050.0510.0850.1270.0130.0000.0000.0000.0500.0780.1900.1240.0000.1150.1040.0030.1970.0590.0470.0510.0740.1220.0780.0670.0620.0000.0491.0000.106
trastero0.1350.0290.2880.2760.0240.1530.2240.2440.1250.1710.0000.0000.0000.0510.0490.1570.3550.0000.1420.2050.0060.0960.0460.0340.0540.0520.4410.4120.1790.1820.0140.1880.1061.000

Missing values

2024-02-11T21:13:55.409235image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-11T21:13:56.231688image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

precioarea_construidan_habitacionesn_banosterrazaascensoraire_acondicionadoamuebladoparkingprecio_parkingorientacion_norientacion_sorientacion_eorientacion_otrasteroarmariospiscinaporterojardinduplexestudioaricon_pisocat_n_max_pisoscat_n_vecinoscat_calidaddistancia_puerta_soldistancia_metrodistancia_castellanalongitudlatitudinteriorstatus_inmuebleantiguidade
012600047110113010000111110001.073193.08.0584290.8720756.868677-3.76693340.3624851313
123500054110003010000010000001.05113.00.8763690.1163821.544125-3.71072540.42243002118
237300075210013010100110000003.06263.00.9074790.1391091.608444-3.71157140.42219012103
422800050010003010000000000100.05197.01.2502310.3370981.794136-3.71434040.4087411288
5498000127320103010000000000003.05183.00.5417730.1614361.168126-3.70752240.41263913118
622500035010103010000000000102.06153.00.8595650.1269951.517437-3.71039540.4224500276
7365000100211103010000000100004.06266.01.3461150.2634451.762922-3.71412640.4074091258
842500070110101110001000000002.05161.00.7535750.4371911.548310-3.71239040.41487012118
1020300044010112010010010000105.05132.00.6546630.1611721.401318-3.70954940.4205090268
1121300040110003010000000000002.06215.00.8984780.3496951.581541-3.71225040.4117270251
precioarea_construidan_habitacionesn_banosterrazaascensoraire_acondicionadoamuebladoparkingprecio_parkingorientacion_norientacion_sorientacion_eorientacion_otrasteroarmariospiscinaporterojardinduplexestudioaricon_pisocat_n_max_pisoscat_n_vecinoscat_calidaddistancia_puerta_soldistancia_metrodistancia_castellanalongitudlatitudinteriorstatus_inmuebleantiguidade
9480517400085020113110001000011101.03675.09.5039150.2943558.392438-3.59313340.4028231211
94806229000120321113110001010011000.03675.09.4940480.2700048.380544-3.59329640.4026151213
94807293000107320113110001111010005.08207.08.4762470.5014797.327070-3.60613140.4002151214
9480819600033010113110000011000102.0114.06.5498430.5548904.857431-3.63143640.4372871210
94809338000103211113111000010010003.05744.08.7591080.5088777.374166-3.60172140.4289771220
94810347000115321113110000111010001.07583.010.0030590.8668268.276950-3.59215440.445810129
9481131100093220113111100111010002.071613.010.1981471.0197888.496364-3.58937640.4450131211
94812342000121221112110010111010003.061073.011.2040271.8836509.573127-3.57627140.443196129
9481314600062310002010000010000003.04175.08.7806920.1717366.941217-3.60869440.4479310248
9481433400093311113011010011010002.093015.011.0704170.2123778.799697-3.58505640.4580981244